Home > Research > Publications & Outputs > Edge flow

Electronic data

  • EdgeFlow_v4_LatesPreview

    Rights statement: ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    4 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Edge flow

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Published
Publication date9/10/2015
Host publicationProceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
PublisherIEEE
Pages1942-1948
Number of pages7
ISBN (Electronic)9781479986965
ISBN (Print)9781479986972
Original languageEnglish

Abstract

In this paper we introduce a new data driven method to novelty detection and object definition in dynamic video streams that indiscriminately detects both static and moving objects in the scene. A sliding window density estimation is introduced in order to reliably detect texture edges. A Sobel
filtering process is used to extract gradient of edges. Using this new approach, the detection of object textures1 can be done accurately and in real-time. In this paper we demonstrate the capabilities of the algorithm on video scenarios, and show that object textures in the scene are reliably detected. We are able to show clearly the capability of the algorithm to be robust in occlusion scenarios; working in real-time, and defining clear objects where other techniques attribute such small detections to
noise.

Bibliographic note

©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.